scRNA-seq of Buteo buteo and Leucocytozoon toddi: Analysis with Seurat (R)
Data files
Jun 05, 2026 version files 3.01 MB
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README.md
8.56 KB
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S1_26-06-04.csv
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S2_26-06-04.csv
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S3_26-06-04.csv
46.97 KB
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S4_26-06-04.csv
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S5_26-06-04.csv
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S6a_26-06-04.csv
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S6b_26-06-04.csv
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Abstract
Parasites and hosts perform fine-tuned cellular and molecular interactions, known from few well-explored model systems. Recent methodological advances allow such deep insights into non-model and phylogenetically remote organisms and their intense reciprocal manipulations within symbioses. We applied single cell RNA-sequencing to blood samples of long-lived wild avian hosts, infected at high and low intensity with malaria-related parasites. We identify diverse parasitic tools for immune evasion and potential manipulation of the avian host immune response. The transcriptomic response of immune cells was decoupled from the infection load, consistent with evidently low pathogenicity. Changes of immune cell populations showed low activation of adaptive immunity, such as clonal expansion of lymphoid cells. This absence of antagonism indicates immunosuppression by the parasite, enabling an adaptive match of life-histories and their long-term coevolution. The identified cell-type specific markers provide an important reference and help to understand the evolutionary forces that shape host immunity in birds. Our study illuminates in rare molecular detail a benign host-parasite interaction and annotates a large coevolutionary gap along the gradient from parasitism to commensalism and mutualism.
Data includes a .Robj file from analysis of scRNA-seq data using the Seurat package in R and supplementary data files describing highlight outputs from the analysis. Six blood samples were subjected to scRNA-seq, 3 representing high infection loads (HP) with Leucocytozoon and 3 representing low infection loads (LP). First using the Seurat package in R, Seurat clusters (cell transcriptome clusters) were identified. Differential gene expression analyses implemented in Seurat were run, comparing each cluster with all other clusters to reveal marker genes for the clusters (MGs).
The HP and LP groups were subjected to differential gene expression analysis for each Seurat cluster (DEGs HP vs LP) revealing parasite load dependent cell-cluster-specific gene expression changes.
Relevant genes were identified based on their function and differential expression strength.
Description of the data and file structure
All relevant analyses can be reproduced using the .Robj file. Summaries of relevant outputs and sample metadata can be found in the CSV files. All CSV files are semicolon-delimited.
File list:
S1_26-06-04.csv (Tab. S1)
S2_26-06-04.csv (Tab. S2)
S3_26-06-04.csv (Tab. S3)
S4_26-06-04.csv (Tab. S4)
S5_26-06-04.csv (Tab. S5)
S6a_26-06-04.csv (Tab. S6a)
S6b_26-06-04.csv (Tab. S6b)
Variables
S1_26-06-04.csv (Tab. S1)
MGs (Marker Genes) defining cell clusters
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RefSeq accession = RefSeq accession number (NCBI)
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gene = Gene name
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id = internal gene ID
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p_val = p-value resulting from differential expression analysis comparing expression of a given gene in one cluster to all other clusters (Marker gene analysis implemented in the Seurat package).
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avg_log2FC = average log-2-fold change resulting from differential expression analysis comparing expression of a given gene in one cluster to all other cluster (Marker gene analysis implemented in the Seurat package).
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pct.1 = percentage of cells in focal cluster (see column "cluster") that express the gene
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pct.2 = percentage of cells in all other clusters combined that express the gene
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p_val_adj = Bonferroni-adjusted p-value
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cluster = Seurat Cluster number
S2_26-06-04.csv (Tab. S2)
DEGs (Differentially Expressed Genes) between HP and LP samples
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RefSeq accession = RefSeq accession number (NCBI)
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gene = Gene name
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id = internal gene ID
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p_val = p-value resulting from differential expression analysis comparing expression of a given gene between LP and HP samples.
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avg_log2FC = average log-2-fold change resulting from differential expression analysis comparing expression of a given gene between HP and LP samples.
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pct.1 = percentage of cells in focal cluster (see column "cluster") that express the gene
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pct.2 = percentage of cells in all other clusters combined that express the gene
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p_val_adj = Bonferroni-adjusted p-value
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SeuratCluster = Seurat Cluster number
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negative_log10_p = -log10 of p_val_adj used for volcano plotting
S3_26-06-04.csv (Tab. S3)
PCGs (Parasitaemia-Correlated Genes): Gene expression that is correlated with parasite loads based on linear regression analysis.
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category = parasite or host gene
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id = internal gene ID
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SeuratCluster = Seurat Cluster number
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r_squared = R^2 value from regression
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adj_r_squared = adjusted R^2 value from regression
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sigma = The standard error of the residuals (or residual standard deviation)
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F statistic = The test statistic for the overall model's significance
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df = Degrees of freedom; number of predictors
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logLik = Log-Likelihood
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AIC = Akaike Information Criterion
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BIC = Bayesian Information Criterion
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deviance = Measures the model's lack of fit compared to a "saturated" model (a model with a separate parameter for every observation)
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df_residual = number of independent observations minus the number of parameters estimated
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nobs = Number of observations used to fit the model
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estimate = The calculated point estimate (coefficient) for a specific term/predictor
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std_error = standard error
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t_val = t-value
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p_val = p-value
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fdr = False Discovery Rate. Adjusted (p)-value calculated to control for multiple hypothesis testing.
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DEG = yes = gene also identified as a DEG in Tab. S2
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p_val_adj = Bonferroni-adjusted p-value from Tab. S2
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avg_log2FC = average log-2-fold change from Tab. S2
S4_26-06-04.csv (Tab. S4)
Immune effectors - summary of genes from Tab. S2 that serve as immune effectors, including a description of their expression in response to varying parasite loads (parasitaemia)
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class = Class of immune effector
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name = Gene name
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regulated? (DEG HP vs LP) = description of the type of expression pattern
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RefSeq accession = RefSeq accession number (NCBI)
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gene = Gene name
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id = internal gene ID
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p_val = p-value resulting from differential expression analysis comparing expression of a given gene between LP and HP samples.
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avg_log2FC = average log-2-fold change resulting from differential expression analysis comparing expression of a given gene between HP and LP samples.
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pct.1 = percentage of cells in focal cluster (see column "cluster") that express the gene
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pct.2 = percentage of cells in all other clusters combined that express the gene
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p_val_adj = Bonferroni-adjusted p-value
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SeuratCluster = Seurat Cluster number
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negative_log10_p = -log10 of p_val_adj used for volcano plotting
S5_26-06-04.csv (Tab. S5)
Sample metadata - Metadata of the sampled individuals (Buteo buteo nestlings) infected with Leucocytozoon
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scRNA-sample ID = ID number of scRNA-seq sample
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Latitude (decimal degrees) = Latitude of sampling location in decimal degrees
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Longitude (decimal degrees) = Longitude of sampling location in decimal degrees
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Tree Species = Tree species harbouring the nest from which sampled individuals were taken
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Date = sampling date
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Time = sampling time
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Ring Number = Ring number (unique identifier) of sampled individual
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parasitemia category in scRNA-seq analyses = category of infection load high versus low
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L. toddi score (1 to 4) = parasite scoring based on microscopy according to https://doi.org/10.3389/fevo.2025.1581179
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Sex = sex of sampled individual
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Age = age of sampled individual (always juvenile)
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Morph = morph of Buteo buteo nestling (dark/intermediate/light)
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Weight [g] = weight of sampled individual in grams
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Wing [cm] = wing length of sampled individual in centimeters
S6a_26-06-04.csv (Tab. S6a)
Cell type proportions (comparison of cell type proportions identified by scRNA-seq versus microscopy)
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sample = ID number of scRNA-seq sample
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cell type 1 = cell type identified by microscopy
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n 1 = number of cells belonging to "cell type 1" counted via microscopy
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n sample 1 = total number of cells counted via microscopy
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n WBC + mature gametocytes 1 = number of white blood cells and mature gametocytes counted via microscopy
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% of cells 1 = "n 1"/"n sample 1"x100
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% of WBC + mature gametocytes 1 = "n WBC + mature gametocytes 1"/"n sample 1"x100
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class 1 = type of quantification (microscopy result)
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cell type 2 = cell type identified via scRNA-seq
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n 2 = number of cells belonging to "cell type 2" counted via scRNA-seq
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n sample 2 = total number of cells counted via scRNA-seq
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n WBC + mature gametocytes 2 = number of white blood cells and mature gametocytes counted via scRNA-seq
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% of cells 2 = "n 2"/"n sample 2"x100
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% of WBC + mature gametocytes 2 = number of white blood cells and mature gametocytes counted via scRNA-seq
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Seurat Clusters 2 = Seurat clusters summarised under "cell type 2"
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class 2 = type of quantification (scRNA-seq)
S6b_26-06-04.csv (Tab. S6b)
Raw cell counts per cluster
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sample = ID number of scRNA-seq sample
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Seurat Cluster = Seurat Cluster number
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n = number of cells counted via scRNA-seq
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n sample = total number of cells counted via scRNA-seq
Sharing/Access information
A detailed description of methods can be found in the linked preprint under https://doi.org/10.1101/2025.03.14.643032
Code/Software
The .Robj-file can be run via R and R-Studio.
scRNA-seq of blood samples from Buteo buteo nestlings infected with Leucocytozoon toddi
